Читать книгу The Handbook of Technical Analysis + Test Bank - Lim Mark Andrew - Страница 14

There are three main approaches to predicting potential future price action or behavior, namely via:

1. Fundamental Analysis

2. Technical Analysis

3. Information Analysis

See Figure 1.2.

Figure 1.2 Three Approaches to Price Forecasting.

Forecasting Stock Prices Using Fundamental Analysis

One way to gauge the potential price of a stock is by analyzing the company’s performance via its financial statements and accounts in order to determine its intrinsic value or the worth of the security in light of all its holdings, debt, earnings, dividends, income and balance sheet activity, cash flow, and so on. This accounting information is normally represented in ratio form, as in price to earnings (P/E), price to earnings growth (PEG), price to book, price to sales, and debt to equity ratios, to name but a few.

The logic is that a strongly performing company should continue to perform well into the future and garner more demand from investors excited to participate in the expected capital gains derived from the stock’s price and appreciating dividend yields. The price of a stock is expected to rise if there are sufficient buyers, signifying a demand for it. Conversely, the price of a stock is expected to decline if there are sufficient sellers, signifying an oversupply in the stock. Demand is potentially generated if the current stock price is below its estimated intrinsic value, that is, it is currently undervalued or underpriced, whereas supply is created if the current stock price is above its estimated intrinsic value, that is, it is currently overvalued or overpriced. See Figures 1.3 and 1.4 for illustrations of using intrinsic value to forecast potential stock price movements.

Figure 1.3 Price Forecasting Based on Intrinsic Value of a Stock.

Figure 1.4 Price Forecasting Based on Intrinsic Value of a Stock.

There are various ways to determine the degree of over- or undervaluation in a stock, some of which include comparing P/E and earnings per share (EPS) ratios or investigating to what extent a stock is trading at a premium or discount in relation to its net current asset value, debt, and other fundamentals. Fundamental analysis helps provide indications as to which stocks to buy based on prior company performance, that is, over the last accounting period. Some investors resort to more active asset-allocation methods to try to time the market for a suitable stock to buy into or get out of, rather than just relying on the traditional buy-and-hold strategy. They resort to studying broad market factors and sector-rotation models in order to buy into the best fundamentally performing stocks within a strengthening industry or sector. This method is popularly termed the top-down approach to investing. A bottom-up approach relies more on a specific company’s fundamental performance. A buy-and-hold strategy in today’s volatile markets may not represent the most effective way of maximizing returns while minimizing potential risks. As a result, many fundamentalists frequently look to various asset pricing and modern portfolio models like the Capital Asset Pricing Model (CAPM) to try to achieve the best balance between risk and expected returns over a risk-free rate (along what is called the efficient frontier).

One of the problems with fundamental analysis is the credibility, reliability, and accuracy of the accounting practices and financial reporting, which is susceptible to manipulation and false or fraudulent reporting. There are various unscrupulous ways to dress up a poorly performing company or financial institution. A simple Internet search will reveal numerous past and ongoing investigations related to such practices. The other problem is the delay in the financial reporting of a company’s current financial state in the market. By the time the next audited report is completed and published, the information is already outdated. It does not furnish timely information to act upon, especially in volatile market environments, and, as a result, does not directly account or adjust for current or sudden developments in the market environment. Nevertheless, fundamental analysis does give valuable information about specific securities and their performances. Its main weakness is its inability to provide clear and specific short-term price levels for traders to act on. Therefore, fundamental information is better suited to longer-term investment decisions, as opposed to short-term market participation, where short-term price fluctuations and precise market timing may be of lesser importance.

Fundamental data, on a broader scale, accounts for the overall underlying economic performance of the markets. Supply and demand reacts to the economic data released at regular intervals, which include interest rate announcements and central bank monetary policy and intervention. One example of how supply and demand in the markets are affected by such factors is the Swiss National Bank’s (SNB) decision to maintain a 1.2000 ceiling on the foreign exchanges rate of the EURCHF, with respect to the Swiss Franc. This creates a technical demand for the Euro (and a corresponding supply in the CHF) around the 1.2000 exchange-rate level. Many traders have acted and are still acting on this policy decision to their advantage, buying every time the rate approaches 1.2000, with stops placed at a reasonable distance below this threshold. The integrity of this artificial ceiling remains intact as long as the SNB stands steadfast by their policy decision to uphold the ceiling at all costs. See Figure 1.5.

Figure 1.5 SNB Policy Impacting on the Value of the CHF.

Source: MetaTrader 4

It behooves the analyst and investor to examine the actual decision-making process involved with investing in a stock based on intrinsic value. While it does provide an indication, with all else being equal, of the integrity of a certain stock relative to the universe of stocks available, there is a disruptive behavioral component that affects this process. It is not just the calculated or estimated intrinsic value that is an important element but also the general perception or future expectation of this value that plays an arguably greater and more significant role in determining the actual share price of a stock. This may explain why shares prices do not always reflect the actual value of a stock. This disagreement between price and value is the result of divergence between the actual intrinsic value and perceived or projected value.

Forecasting Stock Prices Using Information

Generally, information may be gleaned from various public sources such as newspaper reports, magazines, online bulletins, and so on, upon which market participants may then formulate an opinion about the market, making their own predictions about potential market action. Unfortunately, such publicly available information usually has little merit when used for forecasting purposes, as those more privy to non-public material information would have already moved the markets substantially, leaving only an inconsequential amount of action for latecomers to profit from, at the very most. This is where technical analysts have the unfair advantage of observing the markets moving on the charts and immediately taking action, regardless of the cause or reasons why such action exists. They are only interested in the effects such activity has on price. Technical analysts typically do not wait for news to be public knowledge prior to taking action or making a forecast based on a significant price breakout.

The use of non-public material information potentially affords insiders substantial financial gain from such knowledge, as the release of critical or highly sensitive company information may cause a substantial change in the company’s stock price. Hence it is no great feat to be able to forecast potential market direction based on such prior knowledge, especially if the non-public material information is highly significant or headline worthy. Needless to say, insider trading is illegal in the equity markets. But the possibility will always exist that it can occur and in fact has on many occasions. Unfortunately, in unregulated over-the-counter (OTC) markets, nothing stops brokers from front running large client orders, which is just another form of insider trading.

Forecasting Stock Prices Using Technical Analysis

Technical analysis is essentially the identification and forecasting of potential market behavior based largely on the action and dynamics of the market itself. The action and dynamics of the market is best captured via price, volume, and open interest action. The charts provide a visual description of what has transpired in the markets and technical analysts use this past information to infer potential future price action, based on the assumption that price patterns tend to repeat or behave in a reasonably reliable and predictable manner. Let us turn our attention to some popular definitions of technical analysis.

The following definition of technical analysis tells us that charting is the main tool used to forecast potential future price action.

Technical analysis is the study of market action, primarily through the use of charts, for the purpose of forecasting future price trends.

John Murphy, Technical Analysis of the Financial Markets (NYIF, 1999)

The next definition of technical analysis tells us that the charting of past information is used to forecast future price action.

Technical analysis is the science of recording, usually in graphic form, the actual history of trading.. then deducing from that pictured history the probable future trend.

Edwards and Magee, Technical Analysis of Stock Trends (AMACOM, 2007)

Notice that the last two definitions specifically refer to the forecasting of trend action.

It is interesting at this point to draw a parallel here with information used in fundamental analysis. Technical analysis is often criticized for the use of past information as a basis for forecasting future price action, relying on the notion that certain price behaviors tend to repeat. Unfortunately virtually all forms of forecasting are based on the use of prior or past information, which certainly includes statistical-, fundamental-, and behavior-based forecasting. Companies employ accounting data from the most recent and even past quarters as a basis for gauging the current value of a stock. In statistics, regression-line analysis requires the sampling of past data in order to predict probable future values. Even in behavioral finance, the quantitative measure of the market participant’s past actions form the basis for predicting future behavior.

The following definition of technical analysis tells us that it is the study of pure market action and not the fundamentals of the instrument itself.

It refers to the study of the action of the market itself as opposed to the study of the goods in which the market deals.

Edwards and Magee, Technical Analysis of Stock Trends (AMACOM, 2007)

This next definition of technical analysis tells us that it is a form of art, and its purpose is to identify a trend reversal as early as possible.

The art of technical analysis, for it is an art, is to identify a trend reversal at a relatively early stage and ride on that trend until the weight of the evidence shows or proves that the trend has reversed.

Martin Pring, Technical Analysis Explained, 4th Edition (McGraw-Hill, 2002)

The following definition is most relevant in the formulation of trading strategies. It reminds the market participants that nothing is certain and we must weigh our risk and returns.

Technical analysis deals in probabilities, never in certainties.

Martin Pring, Technical Analysis Explained, 4th Edition (McGraw-Hill, 2002)

The next statement gives a behavioral reason as to why technical analysis works.

Technical analysis is based on the assumption that people will continue to make the same mistakes they have made in the past.

Martin Pring, Technical Analysis Explained, 4th Edition (McGraw-Hill, 2002)

This definition by Pring stresses and underscores the point that there is a real reason and explanation as to why past price patterns tend to repeat. The tendency of price to repeat past patterns is mainly attributed to market participants repeating the same behavior. Although it is not impossible with sufficient and continuous conscious effort and strength of will, human beings rarely change their basic behavior, temperament, and deep-rooted biases, especially in relation to their emotional response to fear, greed, hope, anger, and regret when participating in the markets.

The following statement about technical analysis explains its effectiveness in timing early entries and exits.

Market price tends to lead the known fundamentals… Market price acts as a leading indicator of the fundamentals.

John Murphy, Technical Analysis of the Financial Markets (NYIF, 1999)

This definition by Murphy highlights a very important assumption in technical analysis, which is that price is a reflection of all known information acted upon in the markets. It is the sum of all market participants’ trading and investment actions and decisions, including current and future expectations of market action. It also reflects the overall psychology, biases, and beliefs of all market participants. Therefore, the technical analysts believe that the charts tell the whole story and that everything that can or is expected to impact price has already been discounted. This assumption forms the very basis of technical analysis, and without it, technical analysis would be rendered completely pointless.

Fundamental versus Technically Based Market Timing

Before proceeding any further, it is best to briefly explain the meaning of a few commonly used terms in trading and technical analysis:

• To go long means to buy to open a new position

• To liquidate means to sell to close a position previously held

• To go short means to sell to open a new position

• To cover means to buy to close a position previously shorted

Both fundamental and technically based market timing aim to satisfy the same basic principle of buying low and selling high. There are four basic scenarios where this may occur:

1. Long at a low price and liquidate at a higher price

2. Long at a relatively high price and liquidate at an even higher price

3. Short at a high price and cover at a lower price

4. Short at a relatively low price and cover at an even lower price

Listed below are the some of the strengths of each approach with respect to timing the markets.g

Technically Based Market Timing offers the ability to

• Provide precise entry and exit prices

• Provide the precise time of entry and exit

• Provide real-time bullish and bearish signals

• Provide real-time entry and exit price triggers

• Scale in and out based on significant price levels

• Time entries and exits based on volatility behavior of the underlying

• Exit extended trends at technically significant price-reversal levels

• Time entries and exits based on market order flow

• Define percent risk in terms of significant price levels

• Use volume and open interest analysis to gauge strength of an underlying move in order to time entries and exits

• Use market breadth and broad market sentiment to gauge the strength of an underlying move in order to time entries and exits

• Forecast potential peaks (for shorting or liquidating positions) as well as potential troughs (for getting long and covering positions) via the use of cycle and seasonality analysis

Fundamentally Based Market Timing offers the ability to:

• Gauge undervalued stocks with a potential to appreciate in value, but lacking information regarding the precise price or time to get long or to cover

• Gauge overvalued stocks with a risk of depreciating in value, but lacking information regarding the precise price or time to get short or to liquidate

• Screen and participate in fundamentally strong stocks in a sector or industry as part of an active asset allocation or rotation strategy, but lacking information regarding the precise price or time to get long

The Fundamentalist versus Technical Analysts

Listed below are some characteristics of the fundamentalist and technical analyst:

The Fundamentalist:

• Is mainly concerned with intrinsic value

• Strives to understand the underlying causes for potential market moves

• Is focused on which company to participate in

• Can tell you which company to invest in, but cannot tell you the most advantageous moment to start participating in that stock

The Technical Analyst:

• Is mainly concerned with structure and dynamics of market and price action

• Is more concerned with the effects of potential market moves rather than the cause of them

• Cannot usually determine what the intrinsic value of an asset is or whether it is under-or overvalued, but is able to determine precisely when to start participating, purely from the perspective of price performance

• Is not concerned with the underlying factors that led to the rise in price; this is irrelevant for all practical purposes as they believe that price is a reflection of all information available in the markets and therefore that is all that really matters

In short, from what we have covered so far, we know that technical analysis:

• Uses past information

• Uses charts

• Identifies past and current price action

• Forecasts potential future price action based on historical price behavior (especially the start of a new trend)

Technical Data and Information

Technical analysts study market action. Market action itself is mainly comprised of the study of:

• Price action

• Volume action

• Open interest action

• Sentiment

• Market breadth

• Flow of funds

Of all the data that technical analysts employ, price is the most important, followed closely by volume action. Price itself is comprised of an opening, high, low, and closing price, normally referred to as OHLC data. OHLC data normally refers to the daily opening, high, low, and closing prices, but it may be used to denote the OHLC of any bar interval, from 1-minute bars right up to the monthly and yearly bars.

1.4 Classifying Technical Analysis

Technical analysis may be categorized into four distinct branches, that is, classical, statistical, sentiment, and behavioral analysis. Regardless of which branch is employed, all analysis is eventually interpreted via the various behavioral traits, filters, and biases unique to each analyst. Behavioral traits include both the psychological and emotional elements. See Figure 1.6.

Figure 1.6 The Four Branches of Technical Analysis.

Classical technical analysis involves the use of the conventional bar, chart, and Japanese candlestick patterns, oscillator and overlay indicators, as well as market breadth, relative strength, and cycle analysis. Statistical analysis is more quantitative, as opposed to the more qualitative nature of classical technical analysis. It studies the dispersion, central tendencies, skewness, volatility, regression analysis, hypothesis testing, correlation, covariance, and so on. Sentiment analysis is concerned with the psychology of market participants, which includes their emotions and level of optimism or pessimism in the markets. It studies professional and public opinion via polls and questionnaires, trading and investment decisions via flow of funds in the markets, as well as the positions taken by large institutions and hedgers. Finally, behavioral analysis studies the way market participants react to news, profit and losses, the actions of other market participants, and with their own psychological and emotional biases, preferences, and expectations.

Mean Reverting versus Non–Mean Reverting Approach

The type of technical studies employed also depends on the approach taken by traders and analysts with respect to their personal preferences and biases regarding the action of price in the markets. Basically, traders either adopt a contrarian or a momentum-seeking type approach. Being more contrarian in their approach implies that they do not usually expect the price to traverse large distances. In fact they are constantly on the lookout for impending reversals in the markets. In essence, they expect price to be more mean reverting, returning to an average price or balance between supply and demand. Those that adopt the mean-reverting approach prefer to employ technical studies that help pinpoint levels of overbought and oversold activity, which includes divergence analysis, regression analysis, moving average bands, and Bollinger bands. They prefer to trade consolidations rather than trend action. They normally buy at support and short at resistance. Limit entry orders are their preferred mode of order entry. Conversely, being more momentum seeking in their approach implies that they usually expect the price to traverse large distances and for trends to continue to remain intact. They are constantly on the lookout for continuation type breakouts in the markets. In short, they expect price to be more non–mean reverting, where demand creates further demand and supply creates further supply, both driven by a powerful positive-feedback cycle. Those that adopt the non–mean reverting approach prefer to employ technical studies that help pinpoint breakout or trend continuation activity, which includes chart pattern breakouts, moving average breakouts, Darvas Box breakouts, and Donchian channel breakouts. They prefer to trade trends rather than ranging action. They normally short at the breach of support and long at breach of resistance. Stop entry orders are their preferred mode of entry into the markets. See Figure 1.7.

Figure 1.7 Mean Reverting versus Non–Mean Reverting Approaches.

Advantages and Disadvantages of Technical Analysis

The advantages of applying technical analysis to the markets are:

• It is applicable across all markets, instruments, and timeframes, where price patterns, oscillators, and overlay indicators are all treated in exactly the same manner. No new learning is required in order to trade new markets or timeframes, unlike in fundamental analysis where the analyst must be conversant with the specifics of each stock or market.

• There is no need to study the fundamentals of the markets traded or analyzed in order to apply technical analysis, since technical analysts believe that all information that impacts or potentially may impact the stock or market is already reflected in the price on the charts.

• Technical analysis provides a clear visual representation of the behavior of the markets, unlike in fundamental analysis where most of the data is in numerical form.

• It provides timely and precise entry and exit price levels, preceded by technical signals indicating potential bullishness or bearishness. It has the ability to also pinpoint potential time of entry via time projection techniques not available to fundamentalists. Fundamental analysis does not provide the exact price or time of entry.

• It makes the gauging of market risk much easier to visualize. Volatility is more obvious on the charts than it is in numerical form.

• The concerted effort of market participants acting on significantly clear and obvious price triggers in the markets helps create the reaction required for a more reliable trade. This is the consequence of the self-fulfilling prophecy.

The disadvantages of applying technical analysis are:

• It is subjective in its interpretation. A certain price pattern may be perceived in numerous ways. Since every bullish interpretation has an equal and opposite bearish interpretation, all analysis is susceptible to the possibility of interpretational ambiguity. Unfortunately, all manners of interpretation, regardless of the underlying analysis employed – be it fundamental, statistical, or behavioral – are equally subjective in content and form.

• A basic assumption of technical analysis is that price behavior tends to repeat, making it possible to forecast potential future price action. Unfortunately this tendency to repeat may be disrupted by unexpected volatility in the markets caused by geopolitical, economic, or other factors. Popular price patterns may also be distorted by new forms of trade execution that may impact market action, like automated, algorithmic, or high-frequency program trading where trades are initiated in the markets based on non-classical patterns. This interferes with the repeatability of classic chart patterns.

• Charts provide a historical record of price action. It takes practice and experience to be able to identify classical patterns in price. Though this skill can be mastered with enough practice, the art of inferring or forecasting future price action based on past prices is much more difficult to master. The practitioner needs to be intimately familiar with the behavior of price at various timeframes and in different markets. Although classical patterns may be applied equally across all markets and timeframes equally, there is still an element of uniqueness associated with each market action and timeframe.

• It is argued that all market action is essentially a random walk process, and as such applying technical analysis is pointless as all chart patterns arise out of pure chance and are of no significance in the markets. One must remember that if this is the case, then all forms of analysis are ineffective, whether fundamental, statistical, or behavioral. Since the market is primarily driven by perception, we know that the random-walk process is not a true representation of market action, since market participants react in very specific and predictable ways. Though there is always some element of randomness in the markets caused by the uncoordinated actions of a large number of market participants, one can always observe the uncanny accuracy with which price tests and reacts at a psychologically significant barriers or prices. It is hard to believe that price action is the result of random acts of buying and selling by market participants where the participants are totally unencumbered by cost, biases, psychology, or emotion.

• The strong form of the Efficient Market Hypothesis (EMH) argues that since the markets discount all information, price would have already adjusted to the new information and any attempt to profit from such information would be futile. This would render the technical analysis of price action pointless, with the only form of market participation being passive investment. But such efficiency would require that all market participants react instantaneously to all new information in a rational manner. This in itself presents an insurmountable challenge to EMH. The truth is that no system comprising disparate parts in physical reality reacts instantaneously with perfect coordination. Hence it is fairly safe to assume that although absolute market efficiency is not attainable, the market does continually adjust to new information, but at a much lower and less-efficient rate of data discounting. Therefore, technical analysis remains a valid form of market investigation until the markets attain a state of absolute and perfect efficiency.

• Another argument against technical analysis is the idea of the Self-Fulfilling Prophecy (SFP). Proponents of the concept contend that prices react to technical signals not because the signals themselves are important or significant, but rather because of the concerted effort of market participants acting on those signals that make it work. This may in fact be advantageous to the market participants. The trick is in knowing which technical signals would be supported by a large concerted action. The logical answer would be to select only the most significantly clear and obvious technical signals and triggers. Of course, one can further argue that such signals, if they appear to be reliable indicators of support and resistance, would begin to attract an increasing number of traders as time passes. This would eventually lead to traders vying with each other for the best and most cost-effective fills. What seems initially like the concerted action of all market participants now turns into competition with each other. Getting late fills would be costly as well as reduce or wipe out any potential for profit. This naturally results in traders attempting to preempt each other for the best fills. Traders start vying for progressively earlier entries as price approaches the targeted entry levels, leading finally to entries that are too distant from the original entry levels, increasing risk and reducing any potential profits. This disruptive feedback cycle eventually erodes the reliability of the signals, as price fails to react at the expected technical levels. Price finally begins to react reliably again at the expected technical levels as traders stop preempting each other and abandon or disregard the strategy that produced the signals. The process repeats. Therefore, SFP may result in technical signals evolving in a kind of six-stage duty cycle, where the effects of SFP may be advantageous and desirable to traders in the early stages but eventually result in forcing traders into untenable positions. See Figure 1.8.

Figure 1.8 The Idealized Six-Stage Self-Fulfilling Prophecy Cycle.